Overview

Dataset statistics

Number of variables23
Number of observations106644
Missing cells237695
Missing cells (%)9.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.7 MiB
Average record size in memory184.0 B

Variable types

Categorical5
Numeric16
Boolean2

Warnings

Date has a high cardinality: 3399 distinct values High cardinality
MinTemp is highly correlated with MaxTemp and 2 other fieldsHigh correlation
MaxTemp is highly correlated with MinTemp and 5 other fieldsHigh correlation
Evaporation is highly correlated with MaxTemp and 3 other fieldsHigh correlation
Sunshine is highly correlated with Humidity3pm and 2 other fieldsHigh correlation
WindGustSpeed is highly correlated with WindSpeed9am and 1 other fieldsHigh correlation
WindSpeed9am is highly correlated with WindGustSpeed and 1 other fieldsHigh correlation
WindSpeed3pm is highly correlated with WindGustSpeed and 1 other fieldsHigh correlation
Humidity9am is highly correlated with MaxTemp and 3 other fieldsHigh correlation
Humidity3pm is highly correlated with MaxTemp and 5 other fieldsHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Cloud9am is highly correlated with Sunshine and 2 other fieldsHigh correlation
Cloud3pm is highly correlated with Sunshine and 2 other fieldsHigh correlation
Temp9am is highly correlated with MinTemp and 3 other fieldsHigh correlation
Temp3pm is highly correlated with MinTemp and 5 other fieldsHigh correlation
MinTemp is highly correlated with MaxTemp and 3 other fieldsHigh correlation
MaxTemp is highly correlated with MinTemp and 3 other fieldsHigh correlation
Evaporation is highly correlated with MinTemp and 4 other fieldsHigh correlation
Sunshine is highly correlated with Humidity9am and 4 other fieldsHigh correlation
WindGustSpeed is highly correlated with WindSpeed9am and 1 other fieldsHigh correlation
WindSpeed9am is highly correlated with WindGustSpeedHigh correlation
WindSpeed3pm is highly correlated with WindGustSpeedHigh correlation
Humidity9am is highly correlated with Evaporation and 2 other fieldsHigh correlation
Humidity3pm is highly correlated with Sunshine and 4 other fieldsHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Cloud9am is highly correlated with Sunshine and 2 other fieldsHigh correlation
Cloud3pm is highly correlated with Sunshine and 2 other fieldsHigh correlation
Temp9am is highly correlated with MinTemp and 3 other fieldsHigh correlation
Temp3pm is highly correlated with MinTemp and 5 other fieldsHigh correlation
MinTemp is highly correlated with MaxTemp and 2 other fieldsHigh correlation
MaxTemp is highly correlated with MinTemp and 3 other fieldsHigh correlation
Rainfall is highly correlated with MaxTempHigh correlation
Sunshine is highly correlated with Cloud9am and 1 other fieldsHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Cloud9am is highly correlated with SunshineHigh correlation
Cloud3pm is highly correlated with SunshineHigh correlation
Temp9am is highly correlated with MinTemp and 2 other fieldsHigh correlation
Temp3pm is highly correlated with MinTemp and 2 other fieldsHigh correlation
Cloud3pm is highly correlated with RainTomorrow and 3 other fieldsHigh correlation
Temp9am is highly correlated with Pressure3pm and 6 other fieldsHigh correlation
Pressure3pm is highly correlated with Temp9am and 2 other fieldsHigh correlation
WindGustDir is highly correlated with Location and 2 other fieldsHigh correlation
Temp3pm is highly correlated with Temp9am and 5 other fieldsHigh correlation
MaxTemp is highly correlated with Temp9am and 5 other fieldsHigh correlation
Location is highly correlated with Temp9am and 8 other fieldsHigh correlation
WindGustSpeed is highly correlated with WindSpeed3pm and 1 other fieldsHigh correlation
Pressure9am is highly correlated with Temp9am and 2 other fieldsHigh correlation
MinTemp is highly correlated with Temp9am and 5 other fieldsHigh correlation
RainToday is highly correlated with Humidity3pmHigh correlation
WindSpeed3pm is highly correlated with WindGustSpeed and 1 other fieldsHigh correlation
Humidity9am is highly correlated with Temp9am and 6 other fieldsHigh correlation
WindSpeed9am is highly correlated with WindGustSpeed and 1 other fieldsHigh correlation
RainTomorrow is highly correlated with Cloud3pm and 2 other fieldsHigh correlation
WindDir9am is highly correlated with WindGustDir and 2 other fieldsHigh correlation
WindDir3pm is highly correlated with WindGustDir and 2 other fieldsHigh correlation
Sunshine is highly correlated with Cloud3pm and 4 other fieldsHigh correlation
Cloud9am is highly correlated with Cloud3pm and 3 other fieldsHigh correlation
Humidity3pm is highly correlated with Cloud3pm and 8 other fieldsHigh correlation
Evaporation has 45670 (42.8%) missing values Missing
Sunshine has 50926 (47.8%) missing values Missing
WindGustDir has 6984 (6.5%) missing values Missing
WindGustSpeed has 6942 (6.5%) missing values Missing
WindDir9am has 7478 (7.0%) missing values Missing
WindDir3pm has 2856 (2.7%) missing values Missing
WindSpeed3pm has 1991 (1.9%) missing values Missing
Humidity9am has 1317 (1.2%) missing values Missing
Humidity3pm has 2712 (2.5%) missing values Missing
Pressure9am has 10537 (9.9%) missing values Missing
Pressure3pm has 10521 (9.9%) missing values Missing
Cloud9am has 40341 (37.8%) missing values Missing
Cloud3pm has 42953 (40.3%) missing values Missing
Temp3pm has 2045 (1.9%) missing values Missing
Rainfall has 67803 (63.6%) zeros Zeros
Sunshine has 1752 (1.6%) zeros Zeros
WindSpeed9am has 6441 (6.0%) zeros Zeros
Cloud9am has 6411 (6.0%) zeros Zeros
Cloud3pm has 3684 (3.5%) zeros Zeros

Reproduction

Analysis started2021-08-05 07:29:58.351888
Analysis finished2021-08-05 07:56:58.858948
Duration27 minutes and 0.51 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY

Distinct3399
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size833.3 KiB
2013/6/10
 
46
2015/8/9
 
44
2016/10/6
 
44
2013/12/16
 
44
2017/1/9
 
44
Other values (3394)
106422 

Length

Max length10
Median length9
Mean length8.943981846
Min length8

Characters and Unicode

Total characters953822
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)0.1%

Sample

1st row2012/1/19
2nd row2015/4/13
3rd row2010/8/5
4th row2013/3/18
5th row2011/2/16

Common Values

ValueCountFrequency (%)
2013/6/1046
 
< 0.1%
2015/8/944
 
< 0.1%
2016/10/644
 
< 0.1%
2013/12/1644
 
< 0.1%
2017/1/944
 
< 0.1%
2016/5/1744
 
< 0.1%
2013/10/1243
 
< 0.1%
2015/6/2443
 
< 0.1%
2017/2/2043
 
< 0.1%
2016/5/743
 
< 0.1%
Other values (3389)106206
99.6%

Length

2021-08-05T15:56:59.312322image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013/6/1046
 
< 0.1%
2015/8/944
 
< 0.1%
2016/10/644
 
< 0.1%
2013/12/1644
 
< 0.1%
2017/1/944
 
< 0.1%
2016/5/1744
 
< 0.1%
2013/10/1243
 
< 0.1%
2015/6/2443
 
< 0.1%
2017/2/2043
 
< 0.1%
2016/5/743
 
< 0.1%
Other values (3389)106206
99.6%

Most occurring characters

ValueCountFrequency (%)
/213288
22.4%
1195211
20.5%
2179269
18.8%
0152351
16.0%
337600
 
3.9%
533150
 
3.5%
632928
 
3.5%
432031
 
3.4%
931255
 
3.3%
725764
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number740534
77.6%
Other Punctuation213288
 
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1195211
26.4%
2179269
24.2%
0152351
20.6%
337600
 
5.1%
533150
 
4.5%
632928
 
4.4%
432031
 
4.3%
931255
 
4.2%
725764
 
3.5%
820975
 
2.8%
Other Punctuation
ValueCountFrequency (%)
/213288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common953822
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/213288
22.4%
1195211
20.5%
2179269
18.8%
0152351
16.0%
337600
 
3.9%
533150
 
3.5%
632928
 
3.5%
432031
 
3.4%
931255
 
3.3%
725764
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII953822
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/213288
22.4%
1195211
20.5%
2179269
18.8%
0152351
16.0%
337600
 
3.9%
533150
 
3.5%
632928
 
3.5%
432031
 
3.4%
931255
 
3.3%
725764
 
2.7%

Location
Categorical

HIGH CORRELATION

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size833.3 KiB
Canberra
 
2540
Sydney
 
2502
Perth
 
2404
Darwin
 
2384
Hobart
 
2384
Other values (44)
94430 

Length

Max length16
Median length8
Mean length8.700526987
Min length4

Characters and Unicode

Total characters927859
Distinct characters40
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMountGinini
2nd rowNhil
3rd rowNuriootpa
4th rowAdelaide
5th rowSale

Common Values

ValueCountFrequency (%)
Canberra2540
 
2.4%
Sydney2502
 
2.3%
Perth2404
 
2.3%
Darwin2384
 
2.2%
Hobart2384
 
2.2%
Brisbane2384
 
2.2%
Adelaide2328
 
2.2%
SydneyAirport2295
 
2.2%
Watsonia2277
 
2.1%
Mildura2276
 
2.1%
Other values (39)82870
77.7%

Length

2021-08-05T15:56:59.524596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
canberra2540
 
2.4%
sydney2502
 
2.3%
perth2404
 
2.3%
darwin2384
 
2.2%
hobart2384
 
2.2%
brisbane2384
 
2.2%
adelaide2328
 
2.2%
sydneyairport2295
 
2.2%
watsonia2277
 
2.1%
mildura2276
 
2.1%
Other values (39)82870
77.7%

Most occurring characters

ValueCountFrequency (%)
a86798
 
9.4%
r85836
 
9.3%
o79720
 
8.6%
e75757
 
8.2%
n66296
 
7.1%
l57106
 
6.2%
i55788
 
6.0%
t43710
 
4.7%
d27262
 
2.9%
s26957
 
2.9%
Other values (30)322629
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter783943
84.5%
Uppercase Letter143916
 
15.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a86798
11.1%
r85836
10.9%
o79720
10.2%
e75757
9.7%
n66296
 
8.5%
l57106
 
7.3%
i55788
 
7.1%
t43710
 
5.6%
d27262
 
3.5%
s26957
 
3.4%
Other values (12)178713
22.8%
Uppercase Letter
ValueCountFrequency (%)
A19970
13.9%
W17432
12.1%
C13741
9.5%
M12938
9.0%
S11535
8.0%
P11217
7.8%
N9975
6.9%
B9118
6.3%
G8922
6.2%
H6844
 
4.8%
Other values (8)22224
15.4%

Most occurring scripts

ValueCountFrequency (%)
Latin927859
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a86798
 
9.4%
r85836
 
9.3%
o79720
 
8.6%
e75757
 
8.2%
n66296
 
7.1%
l57106
 
6.2%
i55788
 
6.0%
t43710
 
4.7%
d27262
 
2.9%
s26957
 
2.9%
Other values (30)322629
34.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII927859
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a86798
 
9.4%
r85836
 
9.3%
o79720
 
8.6%
e75757
 
8.2%
n66296
 
7.1%
l57106
 
6.2%
i55788
 
6.0%
t43710
 
4.7%
d27262
 
2.9%
s26957
 
2.9%
Other values (30)322629
34.8%

MinTemp
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct385
Distinct (%)0.4%
Missing461
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean12.1861475
Minimum-8.5
Maximum31.9
Zeros118
Zeros (%)0.1%
Negative2549
Negative (%)2.4%
Memory size833.3 KiB
2021-08-05T15:56:59.623509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-8.5
5-th percentile1.8
Q17.6
median12
Q316.8
95-th percentile23
Maximum31.9
Range40.4
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation6.399877034
Coefficient of variation (CV)0.5251763967
Kurtosis-0.4897134661
Mean12.1861475
Median Absolute Deviation (MAD)4.6
Skewness0.02141842667
Sum1293961.7
Variance40.95842605
MonotonicityNot monotonic
2021-08-05T15:56:59.734088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.2705
 
0.7%
10.5664
 
0.6%
11654
 
0.6%
9652
 
0.6%
9.6649
 
0.6%
10645
 
0.6%
8.9642
 
0.6%
11.5636
 
0.6%
12.7634
 
0.6%
10.8634
 
0.6%
Other values (375)99668
93.5%
ValueCountFrequency (%)
-8.51
 
< 0.1%
-8.21
 
< 0.1%
-81
 
< 0.1%
-7.81
 
< 0.1%
-7.62
 
< 0.1%
-7.51
 
< 0.1%
-7.31
 
< 0.1%
-7.21
 
< 0.1%
-7.11
 
< 0.1%
-76
< 0.1%
ValueCountFrequency (%)
31.91
 
< 0.1%
31.41
 
< 0.1%
311
 
< 0.1%
30.71
 
< 0.1%
30.31
 
< 0.1%
30.21
 
< 0.1%
29.91
 
< 0.1%
29.81
 
< 0.1%
29.73
< 0.1%
29.62
< 0.1%

MaxTemp
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct499
Distinct (%)0.5%
Missing231
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean23.23589505
Minimum-4.8
Maximum48.1
Zeros10
Zeros (%)< 0.1%
Negative75
Negative (%)0.1%
Memory size833.3 KiB
2021-08-05T15:56:59.848756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-4.8
5-th percentile12.8
Q117.9
median22.6
Q328.3
95-th percentile35.5
Maximum48.1
Range52.9
Interquartile range (IQR)10.4

Descriptive statistics

Standard deviation7.127716307
Coefficient of variation (CV)0.3067545404
Kurtosis-0.2507746241
Mean23.23589505
Median Absolute Deviation (MAD)5.1
Skewness0.2274692652
Sum2472601.3
Variance50.80433975
MonotonicityNot monotonic
2021-08-05T15:56:59.964814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.4623
 
0.6%
20623
 
0.6%
19.4610
 
0.6%
20.2607
 
0.6%
18.9603
 
0.6%
19.9602
 
0.6%
19602
 
0.6%
20.8601
 
0.6%
18599
 
0.6%
19.5595
 
0.6%
Other values (489)100348
94.1%
ValueCountFrequency (%)
-4.81
< 0.1%
-4.11
< 0.1%
-3.81
< 0.1%
-3.11
< 0.1%
-31
< 0.1%
-2.91
< 0.1%
-2.52
< 0.1%
-2.41
< 0.1%
-2.31
< 0.1%
-2.22
< 0.1%
ValueCountFrequency (%)
48.11
 
< 0.1%
471
 
< 0.1%
46.91
 
< 0.1%
46.82
< 0.1%
46.72
< 0.1%
46.51
 
< 0.1%
46.44
< 0.1%
46.32
< 0.1%
46.13
< 0.1%
462
< 0.1%

Rainfall
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct628
Distinct (%)0.6%
Missing1034
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2.346357353
Minimum0
Maximum268.6
Zeros67803
Zeros (%)63.6%
Negative0
Negative (%)0.0%
Memory size833.3 KiB
2021-08-05T15:57:00.086896image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.6
95-th percentile13
Maximum268.6
Range268.6
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation8.413614353
Coefficient of variation (CV)3.585819672
Kurtosis133.1871606
Mean2.346357353
Median Absolute Deviation (MAD)0
Skewness9.046849764
Sum247798.8
Variance70.78890647
MonotonicityNot monotonic
2021-08-05T15:57:00.198582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
067803
63.6%
0.26483
 
6.1%
0.42830
 
2.7%
0.61918
 
1.8%
0.81522
 
1.4%
11292
 
1.2%
1.21133
 
1.1%
1.41023
 
1.0%
1.6901
 
0.8%
1.8833
 
0.8%
Other values (618)19872
 
18.6%
(Missing)1034
 
1.0%
ValueCountFrequency (%)
067803
63.6%
0.1123
 
0.1%
0.26483
 
6.1%
0.346
 
< 0.1%
0.42830
 
2.7%
0.529
 
< 0.1%
0.61918
 
1.8%
0.711
 
< 0.1%
0.81522
 
1.4%
0.98
 
< 0.1%
ValueCountFrequency (%)
268.61
< 0.1%
247.21
< 0.1%
2401
< 0.1%
236.81
< 0.1%
2251
< 0.1%
219.61
< 0.1%
216.31
< 0.1%
208.51
< 0.1%
206.21
< 0.1%
184.61
< 0.1%

Evaporation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct338
Distinct (%)0.6%
Missing45670
Missing (%)42.8%
Infinite0
Infinite (%)0.0%
Mean5.479720865
Minimum0
Maximum145
Zeros188
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size833.3 KiB
2021-08-05T15:57:00.308957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.6
median4.8
Q37.4
95-th percentile12
Maximum145
Range145
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation4.21177804
Coefficient of variation (CV)0.7686117859
Kurtosis49.57606849
Mean5.479720865
Median Absolute Deviation (MAD)2.4
Skewness3.888045746
Sum334120.5
Variance17.73907426
MonotonicityNot monotonic
2021-08-05T15:57:00.409806image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42468
 
2.3%
81914
 
1.8%
2.21565
 
1.5%
2.61494
 
1.4%
1.81486
 
1.4%
2.41484
 
1.4%
21479
 
1.4%
3.41455
 
1.4%
3.21438
 
1.3%
2.81418
 
1.3%
Other values (328)44773
42.0%
(Missing)45670
42.8%
ValueCountFrequency (%)
0188
 
0.2%
0.14
 
< 0.1%
0.2358
 
0.3%
0.38
 
< 0.1%
0.4567
0.5%
0.511
 
< 0.1%
0.6778
0.7%
0.717
 
< 0.1%
0.81020
1.0%
0.918
 
< 0.1%
ValueCountFrequency (%)
1451
< 0.1%
86.21
< 0.1%
82.41
< 0.1%
77.31
< 0.1%
74.81
< 0.1%
72.21
< 0.1%
70.41
< 0.1%
68.82
< 0.1%
65.81
< 0.1%
65.41
< 0.1%

Sunshine
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct145
Distinct (%)0.3%
Missing50926
Missing (%)47.8%
Infinite0
Infinite (%)0.0%
Mean7.630220037
Minimum0
Maximum14.5
Zeros1752
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size833.3 KiB
2021-08-05T15:57:00.520593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q14.9
median8.5
Q310.7
95-th percentile12.8
Maximum14.5
Range14.5
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation3.785116119
Coefficient of variation (CV)0.496069065
Kurtosis-0.8221447238
Mean7.630220037
Median Absolute Deviation (MAD)2.6
Skewness-0.504584177
Sum425140.6
Variance14.32710403
MonotonicityNot monotonic
2021-08-05T15:57:00.622565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01752
 
1.6%
10.7815
 
0.8%
11809
 
0.8%
10.8801
 
0.8%
10.5765
 
0.7%
10.9760
 
0.7%
10.3753
 
0.7%
10745
 
0.7%
10.2733
 
0.7%
9.8722
 
0.7%
Other values (135)47063
44.1%
(Missing)50926
47.8%
ValueCountFrequency (%)
01752
1.6%
0.1406
 
0.4%
0.2376
 
0.4%
0.3317
 
0.3%
0.4236
 
0.2%
0.5226
 
0.2%
0.6210
 
0.2%
0.7250
 
0.2%
0.8228
 
0.2%
0.9230
 
0.2%
ValueCountFrequency (%)
14.51
 
< 0.1%
14.33
 
< 0.1%
14.22
 
< 0.1%
14.13
 
< 0.1%
1414
 
< 0.1%
13.918
 
< 0.1%
13.842
 
< 0.1%
13.788
0.1%
13.6132
0.1%
13.5127
0.1%

WindGustDir
Categorical

HIGH CORRELATION
MISSING

Distinct16
Distinct (%)< 0.1%
Missing6984
Missing (%)6.5%
Memory size833.3 KiB
W
7316 
SE
6927 
E
6816 
SSE
6780 
N
6780 
Other values (11)
65041 

Length

Max length3
Median length2
Mean length2.195454545
Min length1

Characters and Unicode

Total characters218799
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowE
3rd rowW
4th rowSE
5th rowE

Common Values

ValueCountFrequency (%)
W7316
 
6.9%
SE6927
 
6.5%
E6816
 
6.4%
SSE6780
 
6.4%
N6780
 
6.4%
S6764
 
6.3%
SW6622
 
6.2%
WSW6573
 
6.2%
SSW6454
 
6.1%
NW6062
 
5.7%
Other values (6)32566
30.5%
(Missing)6984
 
6.5%

Length

2021-08-05T15:57:00.811624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w7316
 
7.3%
se6927
 
7.0%
e6816
 
6.8%
n6780
 
6.8%
sse6780
 
6.8%
s6764
 
6.8%
sw6622
 
6.6%
wsw6573
 
6.6%
ssw6454
 
6.5%
nw6062
 
6.1%
Other values (6)32566
32.7%

Most occurring characters

ValueCountFrequency (%)
S58834
26.9%
W56593
25.9%
E53651
24.5%
N49721
22.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter218799
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S58834
26.9%
W56593
25.9%
E53651
24.5%
N49721
22.7%

Most occurring scripts

ValueCountFrequency (%)
Latin218799
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S58834
26.9%
W56593
25.9%
E53651
24.5%
N49721
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII218799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S58834
26.9%
W56593
25.9%
E53651
24.5%
N49721
22.7%

WindGustSpeed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct66
Distinct (%)0.1%
Missing6942
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean39.97222724
Minimum6
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size833.3 KiB
2021-08-05T15:57:00.903478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20
Q131
median39
Q348
95-th percentile65
Maximum135
Range129
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.59954316
Coefficient of variation (CV)0.3402248036
Kurtosis1.393631565
Mean39.97222724
Median Absolute Deviation (MAD)9
Skewness0.8720399573
Sum3985311
Variance184.9475742
MonotonicityNot monotonic
2021-08-05T15:57:01.027315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
356847
 
6.4%
396473
 
6.1%
316211
 
5.8%
375974
 
5.6%
335886
 
5.5%
415327
 
5.0%
305210
 
4.9%
434885
 
4.6%
284794
 
4.5%
444015
 
3.8%
Other values (56)44080
41.3%
(Missing)6942
 
6.5%
ValueCountFrequency (%)
61
 
< 0.1%
715
 
< 0.1%
969
 
0.1%
11144
 
0.1%
13392
 
0.4%
15635
 
0.6%
171060
1.0%
191301
1.2%
201925
1.8%
222089
2.0%
ValueCountFrequency (%)
1352
 
< 0.1%
1301
 
< 0.1%
1262
 
< 0.1%
1221
 
< 0.1%
1202
 
< 0.1%
1172
 
< 0.1%
1154
< 0.1%
1137
< 0.1%
1112
 
< 0.1%
1094
< 0.1%

WindDir9am
Categorical

HIGH CORRELATION
MISSING

Distinct16
Distinct (%)< 0.1%
Missing7478
Missing (%)7.0%
Memory size833.3 KiB
N
8545 
SE
6896 
E
6729 
SSE
6702 
NW
6430 
Other values (11)
63864 

Length

Max length3
Median length2
Mean length2.185718896
Min length1

Characters and Unicode

Total characters216749
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowE
3rd rowN
4th rowE
5th rowNE

Common Values

ValueCountFrequency (%)
N8545
 
8.0%
SE6896
 
6.5%
E6729
 
6.3%
SSE6702
 
6.3%
NW6430
 
6.0%
S6369
 
6.0%
SW6193
 
5.8%
W6169
 
5.8%
NNE5979
 
5.6%
NNW5897
 
5.5%
Other values (6)33257
31.2%
(Missing)7478
 
7.0%

Length

2021-08-05T15:57:01.222386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n8545
 
8.6%
se6896
 
7.0%
e6729
 
6.8%
sse6702
 
6.8%
nw6430
 
6.5%
s6369
 
6.4%
sw6193
 
6.2%
w6169
 
6.2%
nne5979
 
6.0%
nnw5897
 
5.9%
Other values (6)33257
33.5%

Most occurring characters

ValueCountFrequency (%)
N55493
25.6%
S55013
25.4%
E54836
25.3%
W51407
23.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter216749
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N55493
25.6%
S55013
25.4%
E54836
25.3%
W51407
23.7%

Most occurring scripts

ValueCountFrequency (%)
Latin216749
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N55493
25.6%
S55013
25.4%
E54836
25.3%
W51407
23.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII216749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N55493
25.6%
S55013
25.4%
E54836
25.3%
W51407
23.7%

WindDir3pm
Categorical

HIGH CORRELATION
MISSING

Distinct16
Distinct (%)< 0.1%
Missing2856
Missing (%)2.7%
Memory size833.3 KiB
SE
7939 
W
7484 
S
7217 
WSW
6993 
SSE
6899 
Other values (11)
67256 

Length

Max length3
Median length2
Mean length2.208097275
Min length1

Characters and Unicode

Total characters229174
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNW
2nd rowNNE
3rd rowW
4th rowWSW
5th rowE

Common Values

ValueCountFrequency (%)
SE7939
 
7.4%
W7484
 
7.0%
S7217
 
6.8%
WSW6993
 
6.6%
SSE6899
 
6.5%
SW6843
 
6.4%
WNW6518
 
6.1%
N6476
 
6.1%
NW6384
 
6.0%
ESE6301
 
5.9%
Other values (6)34734
32.6%

Length

2021-08-05T15:57:01.411520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se7939
 
7.6%
w7484
 
7.2%
s7217
 
7.0%
wsw6993
 
6.7%
sse6899
 
6.6%
sw6843
 
6.6%
wnw6518
 
6.3%
n6476
 
6.2%
nw6384
 
6.2%
ese6301
 
6.1%
Other values (6)34734
33.5%

Most occurring characters

ValueCountFrequency (%)
S60937
26.6%
W59411
25.9%
E56254
24.5%
N52572
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter229174
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S60937
26.6%
W59411
25.9%
E56254
24.5%
N52572
22.9%

Most occurring scripts

ValueCountFrequency (%)
Latin229174
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S60937
26.6%
W59411
25.9%
E56254
24.5%
N52572
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII229174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S60937
26.6%
W59411
25.9%
E56254
24.5%
N52572
22.9%

WindSpeed9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct42
Distinct (%)< 0.1%
Missing1001
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean13.99292902
Minimum0
Maximum130
Zeros6441
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size833.3 KiB
2021-08-05T15:57:01.501349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median13
Q319
95-th percentile30
Maximum130
Range130
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.873859515
Coefficient of variation (CV)0.6341674073
Kurtosis1.272104202
Mean13.99292902
Median Absolute Deviation (MAD)6
Skewness0.7734114553
Sum1478255
Variance78.74538269
MonotonicityNot monotonic
2021-08-05T15:57:01.607650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
910099
 
9.5%
139670
 
9.1%
118644
 
8.1%
178009
 
7.5%
77928
 
7.4%
157776
 
7.3%
66720
 
6.3%
06441
 
6.0%
196429
 
6.0%
205894
 
5.5%
Other values (32)28033
26.3%
ValueCountFrequency (%)
06441
6.0%
23411
 
3.2%
44723
4.4%
66720
6.3%
77928
7.4%
910099
9.5%
118644
8.1%
139670
9.1%
157776
7.3%
178009
7.5%
ValueCountFrequency (%)
1301
 
< 0.1%
872
 
< 0.1%
743
 
< 0.1%
721
 
< 0.1%
691
 
< 0.1%
673
 
< 0.1%
653
 
< 0.1%
636
< 0.1%
6111
< 0.1%
594
 
< 0.1%

WindSpeed3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct41
Distinct (%)< 0.1%
Missing1991
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean18.6318978
Minimum0
Maximum87
Zeros828
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size833.3 KiB
2021-08-05T15:57:01.707621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q113
median19
Q324
95-th percentile35
Maximum87
Range87
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.805657478
Coefficient of variation (CV)0.4726119462
Kurtosis0.7510760031
Mean18.6318978
Median Absolute Deviation (MAD)6
Skewness0.6306762083
Sum1949884
Variance77.53960363
MonotonicityNot monotonic
2021-08-05T15:57:01.808556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
179243
 
8.7%
139212
 
8.6%
208603
 
8.1%
158597
 
8.1%
198225
 
7.7%
117334
 
6.9%
97143
 
6.7%
246605
 
6.2%
226350
 
6.0%
284786
 
4.5%
Other values (31)28555
26.8%
ValueCountFrequency (%)
0828
 
0.8%
2739
 
0.7%
41658
 
1.6%
62844
 
2.7%
74415
4.1%
97143
6.7%
117334
6.9%
139212
8.6%
158597
8.1%
179243
8.7%
ValueCountFrequency (%)
871
 
< 0.1%
831
 
< 0.1%
741
 
< 0.1%
721
 
< 0.1%
693
 
< 0.1%
6514
< 0.1%
639
< 0.1%
6114
< 0.1%
5916
< 0.1%
5718
< 0.1%

Humidity9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct101
Distinct (%)0.1%
Missing1317
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean68.80237736
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size833.3 KiB
2021-08-05T15:57:01.911735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q157
median70
Q383
95-th percentile98
Maximum100
Range100
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.09367738
Coefficient of variation (CV)0.277514791
Kurtosis-0.04113212112
Mean68.80237736
Median Absolute Deviation (MAD)13
Skewness-0.4846030253
Sum7246748
Variance364.5685157
MonotonicityNot monotonic
2021-08-05T15:57:02.021554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
992490
 
2.3%
652230
 
2.1%
712214
 
2.1%
682204
 
2.1%
692200
 
2.1%
662187
 
2.1%
702184
 
2.0%
642181
 
2.0%
672160
 
2.0%
742135
 
2.0%
Other values (91)83142
78.0%
ValueCountFrequency (%)
01
 
< 0.1%
14
 
< 0.1%
23
 
< 0.1%
39
 
< 0.1%
418
 
< 0.1%
520
 
< 0.1%
632
< 0.1%
731
< 0.1%
840
< 0.1%
956
0.1%
ValueCountFrequency (%)
1002111
2.0%
992490
2.3%
981546
1.4%
971338
1.3%
961181
1.1%
951188
1.1%
941321
1.2%
931335
1.3%
921299
1.2%
911390
1.3%

Humidity3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct101
Distinct (%)0.1%
Missing2712
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean51.43818073
Minimum0
Maximum100
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size833.3 KiB
2021-08-05T15:57:02.135134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q136
median52
Q366
95-th percentile87
Maximum100
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation20.80630037
Coefficient of variation (CV)0.4044913734
Kurtosis-0.5127448091
Mean51.43818073
Median Absolute Deviation (MAD)14
Skewness0.03567659358
Sum5346073
Variance432.9021352
MonotonicityNot monotonic
2021-08-05T15:57:02.253091image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
552045
 
1.9%
532025
 
1.9%
522004
 
1.9%
571995
 
1.9%
591984
 
1.9%
581949
 
1.8%
501939
 
1.8%
541927
 
1.8%
561923
 
1.8%
511916
 
1.8%
Other values (91)84225
79.0%
(Missing)2712
 
2.5%
ValueCountFrequency (%)
02
 
< 0.1%
118
 
< 0.1%
224
 
< 0.1%
344
 
< 0.1%
487
 
0.1%
5121
 
0.1%
6183
0.2%
7222
0.2%
8314
0.3%
9362
0.3%
ValueCountFrequency (%)
100306
0.3%
99314
0.3%
98448
0.4%
97286
0.3%
96337
0.3%
95345
0.3%
94404
0.4%
93438
0.4%
92461
0.4%
91461
0.4%

Pressure9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct532
Distinct (%)0.6%
Missing10537
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean1017.651975
Minimum980.5
Maximum1041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size833.3 KiB
2021-08-05T15:57:02.361755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum980.5
5-th percentile1006.2
Q11013
median1017.6
Q31022.4
95-th percentile1029.5
Maximum1041
Range60.5
Interquartile range (IQR)9.4

Descriptive statistics

Standard deviation7.105074105
Coefficient of variation (CV)0.006981830996
Kurtosis0.2419896874
Mean1017.651975
Median Absolute Deviation (MAD)4.7
Skewness-0.09838386711
Sum97803478.4
Variance50.48207804
MonotonicityNot monotonic
2021-08-05T15:57:02.480214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1017.9604
 
0.6%
1016.4599
 
0.6%
1015.9587
 
0.6%
1018.7586
 
0.5%
1017.3584
 
0.5%
1017.8582
 
0.5%
1016.3577
 
0.5%
1018.6569
 
0.5%
1018569
 
0.5%
1017.7567
 
0.5%
Other values (522)90283
84.7%
(Missing)10537
 
9.9%
ValueCountFrequency (%)
980.51
< 0.1%
982.21
< 0.1%
982.31
< 0.1%
982.91
< 0.1%
983.71
< 0.1%
983.91
< 0.1%
9851
< 0.1%
985.11
< 0.1%
985.91
< 0.1%
986.21
< 0.1%
ValueCountFrequency (%)
10411
 
< 0.1%
1040.91
 
< 0.1%
1040.62
< 0.1%
1040.51
 
< 0.1%
1040.43
< 0.1%
1040.33
< 0.1%
1040.11
 
< 0.1%
10401
 
< 0.1%
1039.92
< 0.1%
1039.53
< 0.1%

Pressure3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct537
Distinct (%)0.6%
Missing10521
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean1015.254297
Minimum977.1
Maximum1039.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size833.3 KiB
2021-08-05T15:57:02.593204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum977.1
5-th percentile1004
Q11010.4
median1015.2
Q31020
95-th percentile1026.9
Maximum1039.6
Range62.5
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation7.035537253
Coefficient of variation (CV)0.006929827605
Kurtosis0.1375229134
Mean1015.254297
Median Absolute Deviation (MAD)4.8
Skewness-0.04559367893
Sum97589288.79
Variance49.49878444
MonotonicityNot monotonic
2021-08-05T15:57:02.700788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1015.5590
 
0.6%
1015.3588
 
0.6%
1015.6582
 
0.5%
1014.9568
 
0.5%
1015.1566
 
0.5%
1015.4562
 
0.5%
1015.8562
 
0.5%
1014.2561
 
0.5%
1015.2558
 
0.5%
1016557
 
0.5%
Other values (527)90429
84.8%
(Missing)10521
 
9.9%
ValueCountFrequency (%)
977.11
< 0.1%
978.21
< 0.1%
9791
< 0.1%
980.22
< 0.1%
981.41
< 0.1%
981.91
< 0.1%
982.61
< 0.1%
983.21
< 0.1%
983.31
< 0.1%
9841
< 0.1%
ValueCountFrequency (%)
1039.61
 
< 0.1%
1038.91
 
< 0.1%
1038.51
 
< 0.1%
1038.41
 
< 0.1%
1038.21
 
< 0.1%
10381
 
< 0.1%
1037.92
< 0.1%
1037.81
 
< 0.1%
1037.73
< 0.1%
1037.61
 
< 0.1%

Cloud9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing40341
Missing (%)37.8%
Infinite0
Infinite (%)0.0%
Mean4.438486946
Minimum0
Maximum9
Zeros6411
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size833.3 KiB
2021-08-05T15:57:02.790288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.886584678
Coefficient of variation (CV)0.6503533103
Kurtosis-1.540334792
Mean4.438486946
Median Absolute Deviation (MAD)3
Skewness-0.2241546001
Sum294285
Variance8.332371105
MonotonicityNot monotonic
2021-08-05T15:57:02.865259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
714732
 
13.8%
111658
 
10.9%
810822
 
10.1%
06411
 
6.0%
66030
 
5.7%
24823
 
4.5%
34369
 
4.1%
54157
 
3.9%
43300
 
3.1%
91
 
< 0.1%
(Missing)40341
37.8%
ValueCountFrequency (%)
06411
6.0%
111658
10.9%
24823
 
4.5%
34369
 
4.1%
43300
 
3.1%
54157
 
3.9%
66030
5.7%
714732
13.8%
810822
10.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
810822
10.1%
714732
13.8%
66030
5.7%
54157
 
3.9%
43300
 
3.1%
34369
 
4.1%
24823
 
4.5%
111658
10.9%
06411
6.0%

Cloud3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing42953
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean4.501515128
Minimum0
Maximum9
Zeros3684
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size833.3 KiB
2021-08-05T15:57:02.949000image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.718442459
Coefficient of variation (CV)0.6038949958
Kurtosis-1.457578751
Mean4.501515128
Median Absolute Deviation (MAD)2
Skewness-0.2224453334
Sum286706
Variance7.389929405
MonotonicityNot monotonic
2021-08-05T15:57:03.020873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
713510
 
12.7%
111101
 
10.4%
89254
 
8.7%
66615
 
6.2%
25367
 
5.0%
35145
 
4.8%
55079
 
4.8%
43935
 
3.7%
03684
 
3.5%
91
 
< 0.1%
(Missing)42953
40.3%
ValueCountFrequency (%)
03684
 
3.5%
111101
10.4%
25367
 
5.0%
35145
 
4.8%
43935
 
3.7%
55079
 
4.8%
66615
6.2%
713510
12.7%
89254
8.7%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
89254
8.7%
713510
12.7%
66615
6.2%
55079
 
4.8%
43935
 
3.7%
35145
 
4.8%
25367
 
5.0%
111101
10.4%
03684
 
3.5%

Temp9am
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct433
Distinct (%)0.4%
Missing661
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean16.98934357
Minimum-7.2
Maximum39.4
Zeros28
Zeros (%)< 0.1%
Negative309
Negative (%)0.3%
Memory size833.3 KiB
2021-08-05T15:57:03.126560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-7.2
5-th percentile6.9
Q112.3
median16.7
Q321.6
95-th percentile28.2
Maximum39.4
Range46.6
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.497897783
Coefficient of variation (CV)0.3824690315
Kurtosis-0.3541248313
Mean16.98934357
Median Absolute Deviation (MAD)4.6
Skewness0.09114657837
Sum1800581.6
Variance42.22267559
MonotonicityNot monotonic
2021-08-05T15:57:03.236530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16673
 
0.6%
13.8657
 
0.6%
17654
 
0.6%
14.8651
 
0.6%
16.5643
 
0.6%
15641
 
0.6%
13639
 
0.6%
14637
 
0.6%
15.6632
 
0.6%
17.8628
 
0.6%
Other values (423)99528
93.3%
(Missing)661
 
0.6%
ValueCountFrequency (%)
-7.21
 
< 0.1%
-71
 
< 0.1%
-5.61
 
< 0.1%
-5.52
< 0.1%
-5.23
< 0.1%
-4.81
 
< 0.1%
-4.52
< 0.1%
-4.41
 
< 0.1%
-4.33
< 0.1%
-4.22
< 0.1%
ValueCountFrequency (%)
39.41
 
< 0.1%
39.11
 
< 0.1%
391
 
< 0.1%
38.91
 
< 0.1%
38.31
 
< 0.1%
38.21
 
< 0.1%
37.72
< 0.1%
37.63
< 0.1%
37.52
< 0.1%
37.42
< 0.1%

Temp3pm
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct494
Distinct (%)0.5%
Missing2045
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean21.69275519
Minimum-5.4
Maximum46.2
Zeros12
Zeros (%)< 0.1%
Negative128
Negative (%)0.1%
Memory size833.3 KiB
2021-08-05T15:57:03.342116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-5.4
5-th percentile11.5
Q116.6
median21.1
Q326.4
95-th percentile33.7
Maximum46.2
Range51.6
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.945127279
Coefficient of variation (CV)0.3201588373
Kurtosis-0.1547407308
Mean21.69275519
Median Absolute Deviation (MAD)4.9
Skewness0.2423354878
Sum2269040.5
Variance48.23479292
MonotonicityNot monotonic
2021-08-05T15:57:03.443419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.5668
 
0.6%
20660
 
0.6%
19639
 
0.6%
18.4638
 
0.6%
17.8636
 
0.6%
19.3628
 
0.6%
17.4614
 
0.6%
17613
 
0.6%
18609
 
0.6%
19.4608
 
0.6%
Other values (484)98286
92.2%
(Missing)2045
 
1.9%
ValueCountFrequency (%)
-5.41
 
< 0.1%
-4.41
 
< 0.1%
-4.21
 
< 0.1%
-41
 
< 0.1%
-3.81
 
< 0.1%
-3.72
< 0.1%
-3.53
< 0.1%
-3.41
 
< 0.1%
-3.24
< 0.1%
-3.12
< 0.1%
ValueCountFrequency (%)
46.21
 
< 0.1%
46.13
< 0.1%
45.91
 
< 0.1%
45.41
 
< 0.1%
45.31
 
< 0.1%
45.21
 
< 0.1%
451
 
< 0.1%
44.91
 
< 0.1%
44.83
< 0.1%
44.73
< 0.1%

RainToday
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing1034
Missing (%)1.0%
Memory size208.4 KiB
False
82065 
True
23545 
(Missing)
 
1034
ValueCountFrequency (%)
False82065
77.0%
True23545
 
22.1%
(Missing)1034
 
1.0%
2021-08-05T15:57:03.513142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

RainTomorrow
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.3 KiB
False
82786 
True
23858 
ValueCountFrequency (%)
False82786
77.6%
True23858
 
22.4%
2021-08-05T15:57:03.547333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Interactions

2021-08-05T15:56:26.382987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:26.524701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:26.646236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:26.756199image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:26.872157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:27.003557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:27.142771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:27.365229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:27.487900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:27.635329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:27.781025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:27.911735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:28.025138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:28.129228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:28.245783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:28.360300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:28.478857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:28.592314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:28.701886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:28.806733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:28.905174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:29.011197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:29.130667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:29.266517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:29.394808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:29.504096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:29.624604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:29.728821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:29.829866image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:29.925980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:30.029837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:30.229519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:30.350623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:30.491019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:30.618444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:30.722290image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:30.823528image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:30.938493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:31.058661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:31.184031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:31.293360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:31.415969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:31.523607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:31.643072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:31.753811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:31.872279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:31.999761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:32.144092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:32.249447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:32.363780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:32.479173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:32.591984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:32.719790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:32.831446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:32.951994image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:33.064159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:33.173816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:33.290036image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:33.414604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:33.517977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:33.637708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:33.850992image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:33.968351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:34.111904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:34.214677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:34.335838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:34.446937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:34.552284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:34.665396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:34.793349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:34.925970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:35.061633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:35.166820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:35.274829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:35.386474image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:35.494074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:35.598680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:35.698926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:35.822618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:35.949412image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:36.074865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:36.201089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:36.312210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:36.421965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:36.539588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:36.671546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:36.808982image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:36.923986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:37.044473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:37.168114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:37.278388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:37.393555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:37.502265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:37.605321image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:37.730395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:37.855882image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:37.966750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:38.204479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:38.320884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:38.424814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:38.528172image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:38.664164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:38.777195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:38.893429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:39.001296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:39.122404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:39.249873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:39.352417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:39.471775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:39.582583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:39.702732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:39.842154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:39.979504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:40.102620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:40.222638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:40.331489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:40.454238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:40.574440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:40.690595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:40.809381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:40.908112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:41.024593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:41.149263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:41.277624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:41.392999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:41.488270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:41.603453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:41.722098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:41.840674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:41.959337image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:42.067769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:42.184941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:42.311289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:42.435853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:42.575185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:42.693062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:42.818390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:42.928530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:43.034699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:43.153812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:43.273810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:43.375001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:43.486028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:43.740088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:43.851066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:43.968333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:44.094669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:44.208004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:44.311768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:44.432205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:44.572879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:44.679647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:44.787632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:44.897050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:45.029413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:45.131086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:45.273314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:45.399040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:45.529845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:45.642372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:45.753392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:45.887468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:46.004884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:46.106758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:46.212237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:46.344874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:46.470293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:46.576770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:46.696394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:46.816970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:46.944087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:47.067780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:47.182076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:47.292559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:47.416196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:47.555124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:47.676390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:47.819125image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:47.929989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:48.042335image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:48.143697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:48.271099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:48.383200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:48.507530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:48.635791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:48.774122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:48.912432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:49.034834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:49.141860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:49.260606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:49.380759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:49.494284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:49.617729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:49.726002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:49.840385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:49.949699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:50.046260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:50.146323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:50.267746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:50.574591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:50.666735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:50.770843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:50.887366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:51.003007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:51.116727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:51.253133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:51.368450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:51.469583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:51.581377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:51.684951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:51.774859image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:51.882554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:51.988978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:52.098981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:52.202907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:52.299946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:52.402478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:52.516524image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:52.633114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:52.729333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:52.865720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:52.984372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:53.088650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:53.190988image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:53.302882image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:53.431611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:53.555503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:53.659981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:53.765454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:53.896932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:54.009950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:54.122989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:54.245908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:54.361667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:54.485096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:54.601137image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:54.714936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:54.810494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:54.919457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:55.026992image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:55.138440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:55.259592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:55.378885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:55.468685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:55.569031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:55.678224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:55.795991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:55.906950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:56.025640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:56.132632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:56.256689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:56.384792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:56.498873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:56.617169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:56.731635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-05T15:56:56.832525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-08-05T15:57:03.614277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-05T15:57:03.789424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-05T15:57:03.990669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-05T15:57:04.168877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-05T15:57:04.349236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-05T15:56:57.064132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-05T15:56:57.540674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-05T15:56:58.278434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-05T15:56:58.641766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
02012/1/19MountGinini12.123.10.0NaNNaNW30.0NNW6.011.060.054.0NaNNaNNaNNaN17.022.0NoNo
12015/4/13Nhil10.224.70.0NaNNaNE39.0ENNE13.09.063.033.01021.91017.9NaNNaN12.523.7NoYes
22010/8/5Nuriootpa-0.411.03.60.41.6W28.0NW4.020.097.078.01025.91025.37.08.03.99.0YesNo
32013/3/18Adelaide13.222.60.015.411.0SE44.0EWSW15.015.047.034.01025.01022.2NaNNaN15.221.7NoNo
42011/2/16Sale14.128.60.06.66.7E28.0NEE4.019.092.042.01018.01014.14.07.019.128.2NoNo
52013/4/16Walpole16.121.60.0NaNNaNE26.0NaNS0.07.094.082.01014.71013.6NaNNaN18.721.0NoNo
62016/8/12Albany8.516.45.02.2NaNNaNNaNNWNaN9.0NaN78.0NaN1023.51022.36.0NaN11.6NaNYesNo
72011/8/7Moree10.819.40.03.81.2NNE33.0NWNW26.07.067.053.01019.71015.27.07.016.119.2NoNo
82009/9/3Bendigo9.020.60.04.0NaNWNW48.0NNEWNW19.07.054.060.01012.31008.61.07.013.215.9NoYes
92014/10/15NorfolkIsland14.021.10.06.66.2ENE37.0NENE15.020.058.080.01022.81021.11.07.019.318.5NoNo

Last rows

DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
1066342014/6/11AliceSprings1.124.40.03.810.3ENE24.0WSWE9.09.042.030.01018.91012.92.02.010.023.2NoNo
1066352009/5/27Mildura6.417.10.01.68.0SSW26.0SSWSW11.017.097.053.01024.51022.31.05.08.416.5NoNo
1066362017/3/16PearceRAAF10.929.00.2NaN11.1E52.0ENE28.013.051.029.01017.71013.0NaNNaN19.928.4NoNo
1066372017/2/19NewcastleNaN23.53.2NaNNaNNaNNaNSWNaN9.0NaN89.0NaNNaNNaN7.0NaN21.0NaNYesNo
1066382013/12/2SalmonGums13.931.00.0NaNNaNSSE48.0ENESW11.09.056.028.0NaNNaNNaNNaN20.928.8NoNo
1066392011/5/23Launceston10.116.115.8NaNNaNSE31.0NNWNNW4.07.099.086.0999.2995.2NaNNaN13.015.6YesYes
1066402014/12/9GoldCoast19.331.736.0NaNNaNSE80.0NNWNNE17.030.075.076.01013.81010.0NaNNaN26.025.8YesYes
1066412014/10/7Wollongong17.522.21.2NaNNaNWNW65.0WNWNE39.019.061.056.01008.21008.2NaNNaN17.821.4YesNo
1066422012/1/16Newcastle17.627.03.0NaNNaNNaNNaNNENE6.015.068.088.0NaNNaN6.05.022.626.4YesNo
1066432014/10/21AliceSprings16.337.90.014.212.2ESE41.0NNENE20.013.08.06.01017.91014.00.01.032.235.7NoNo